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The paper presents a novel Sequence-Aware Vision Transformer (GLF-ViT) designed for fault diagnosis in complex industrial processes, addressing challenges posed by high-dimensional data and intricate temporal dependencies. By fusing global and local features through a modified feature embedding and a multi-head attention mechanism, the proposed method significantly improves diagnostic accuracy compared to traditional approaches. Experimental results demonstrate that GLF-ViT outperforms state-of-the-art algo

Author

arnab

Language

EN